OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking

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May 15, 2023
Fazle Rabbi Rakib, Souhardya Saha Dip, Samiul Alam, Nazia Tasnim, Md. Istiak Hossain Shihab, Md. Nazmuddoha Ansary, Syed Mobassir Hossen, Marsia Haque Meghla, Mamunur Mamun, Farig Sadeque, Sayma Sultana Chowdhury, Tahsin Reasat, Asif Sushmit, Ahmed Imtiaz Humayun

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We present OOD-Speech, the first out-of-distribution (OOD) benchmarking dataset for Bengali automatic speech recognition (ASR). Being one of the most spoken languages globally, Bengali portrays large diversity in dialects and prosodic features, which demands ASR frameworks to be robust towards distribution shifts. For example, islamic religious sermons in Bengali are delivered with a tonality that is significantly different from regular speech. Our training dataset is collected via massively online crowdsourcing campaigns which resulted in 1177.94 hours collected and curated from $22,645$ native Bengali speakers from South Asia. Our test dataset comprises 23.03 hours of speech collected and manually annotated from 17 different sources, e.g., Bengali TV drama, Audiobook, Talk show, Online class, and Islamic sermons to name a few. OOD-Speech is jointly the largest publicly available speech dataset, as well as the first out-of-distribution ASR benchmarking dataset for Bengali.

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